PMID- 33607296
OWN - NLM
STAT- MEDLINE
DCOM- 20220603
LR  - 20220716
IS  - 2210-3244 (Electronic)
IS  - 1672-0229 (Print)
IS  - 1672-0229 (Linking)
VI  - 19
IP  - 5
DP  - 2021 Oct
TI  - kLDM: Inferring Multiple Metagenomic Association Networks Based on the Variation 
      of Environmental Factors.
PG  - 834-847
LID - S1672-0229(21)00020-6 [pii]
LID - 10.1016/j.gpb.2020.06.015 [doi]
AB  - Identification of significant biological relationships or patterns is central to 
      many metagenomic studies. Methods that estimate association networks have been 
      proposed for this purpose; however, they assume that associations are static, 
      neglecting the fact that relationships in a microbial ecosystem may vary with 
      changes in environmental factors (EFs), which can result in inaccurate 
      estimations. Therefore, in this study, we propose a computational model, called 
      the k-Lognormal-Dirichlet-Multinomial (kLDM) model, which estimates multiple 
      association networks that correspond to specific environmental conditions, and 
      simultaneously infers microbe-microbe and EF-microbe associations for each 
      network. The effectiveness of the kLDM model was demonstrated on synthetic data, 
      a colorectal cancer (CRC) dataset, the Tara Oceans dataset, and the American Gut 
      Project dataset. The results revealed that the widely-used Spearman's rank 
      correlation coefficient method performed much worse than the other methods, 
      indicating the importance of separating samples by environmental conditions. 
      Cancer fecal samples were then compared with cancer-free samples, and the 
      estimation achieved by kLDM exhibited fewer associations among microbes but 
      stronger associations between specific bacteria, especially five CRC-associated 
      operational taxonomic units, indicating gut microbe translocation in cancer 
      patients. Some EF-dependent associations were then found within a marine 
      eukaryotic community. Finally, the gut microbial heterogeneity of inflammatory 
      bowel disease patients was detected. These results demonstrate that kLDM can 
      elucidate the complex associations within microbial ecosystems. The kLDM program, 
      R, and Python scripts, together with all experimental datasets, are accessible at 
      https://github.com/tinglab/kLDM.git.
CI  - Copyright (c) 2021 The Authors. Published by Elsevier B.V. All rights reserved.
FAU - Yang, Yuqing
AU  - Yang Y
AD  - Department of Computer Science and Technology and Institute of Artificial 
      Intelligence, Tsinghua University, Beijing 100084, China; Sogou Inc., Beijing 
      100084, China.
FAU - Wang, Xin
AU  - Wang X
AD  - Peking Union Medical College, Chinese Academy of Medical Science, Beijing 100005, 
      China; Department of Ultrasound, Peking Union Medical College Hospital, Beijing 
      100005, China.
FAU - Xie, Kaikun
AU  - Xie K
AD  - Department of Computer Science and Technology and Institute of Artificial 
      Intelligence, Tsinghua University, Beijing 100084, China.
FAU - Zhu, Congmin
AU  - Zhu C
AD  - Department of Computer Science and Technology and Institute of Artificial 
      Intelligence, Tsinghua University, Beijing 100084, China; Beijing National 
      Research Center for Information Science and Technology, Beijing 100084, China.
FAU - Chen, Ning
AU  - Chen N
AD  - Department of Computer Science and Technology and Institute of Artificial 
      Intelligence, Tsinghua University, Beijing 100084, China. Electronic address: 
      ningchen@tsinghua.edu.cn.
FAU - Chen, Ting
AU  - Chen T
AD  - Department of Computer Science and Technology and Institute of Artificial 
      Intelligence, Tsinghua University, Beijing 100084, China; Department of 
      Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal 
      Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 
      350122, China; Beijing National Research Center for Information Science and 
      Technology, Beijing 100084, China. Electronic address: tingchen@tsinghua.edu.cn.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210217
PL  - England
TA  - Genomics Proteomics Bioinformatics
JT  - Genomics, proteomics & bioinformatics
JID - 101197608
SB  - IM
MH  - Algorithms
MH  - Computational Biology/methods
MH  - Humans
MH  - Metagenome
MH  - Metagenomics/methods
MH  - *Microbiota/genetics
PMC - PMC9170748
OTO - NOTNLM
OT  - Association inference
OT  - Bayesian model
OT  - Clustering
OT  - Environmental condition
OT  - Metagenomics
COIS- Competing interests All authors declare no conflicts of interest.
EDAT- 2021/02/20 06:00
MHDA- 2022/06/07 06:00
PMCR- 2021/02/17
CRDT- 2021/02/19 20:11
PHST- 2019/01/08 00:00 [received]
PHST- 2020/04/16 00:00 [revised]
PHST- 2020/08/15 00:00 [accepted]
PHST- 2021/02/20 06:00 [pubmed]
PHST- 2022/06/07 06:00 [medline]
PHST- 2021/02/19 20:11 [entrez]
PHST- 2021/02/17 00:00 [pmc-release]
AID - S1672-0229(21)00020-6 [pii]
AID - 10.1016/j.gpb.2020.06.015 [doi]
PST - ppublish
SO  - Genomics Proteomics Bioinformatics. 2021 Oct;19(5):834-847. doi: 
      10.1016/j.gpb.2020.06.015. Epub 2021 Feb 17.